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  <h1>Source code for pyspark.ml.tuning</h1><div class="highlight"><pre>
<span class="c">#</span>
<span class="c"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
<span class="c"># contributor license agreements.  See the NOTICE file distributed with</span>
<span class="c"># this work for additional information regarding copyright ownership.</span>
<span class="c"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
<span class="c"># (the &quot;License&quot;); you may not use this file except in compliance with</span>
<span class="c"># the License.  You may obtain a copy of the License at</span>
<span class="c">#</span>
<span class="c">#    http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c">#</span>
<span class="c"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c"># See the License for the specific language governing permissions and</span>
<span class="c"># limitations under the License.</span>
<span class="c">#</span>

<span class="kn">import</span> <span class="nn">itertools</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>

<span class="kn">from</span> <span class="nn">pyspark.ml.param</span> <span class="kn">import</span> <span class="n">Params</span><span class="p">,</span> <span class="n">Param</span>
<span class="kn">from</span> <span class="nn">pyspark.ml</span> <span class="kn">import</span> <span class="n">Estimator</span><span class="p">,</span> <span class="n">Model</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.util</span> <span class="kn">import</span> <span class="n">keyword_only</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.functions</span> <span class="kn">import</span> <span class="n">rand</span>

<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s">&#39;ParamGridBuilder&#39;</span><span class="p">,</span> <span class="s">&#39;CrossValidator&#39;</span><span class="p">,</span> <span class="s">&#39;CrossValidatorModel&#39;</span><span class="p">]</span>


<div class="viewcode-block" id="ParamGridBuilder"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.ParamGridBuilder">[docs]</a><span class="k">class</span> <span class="nc">ParamGridBuilder</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="sd">r&quot;&quot;&quot;</span>
<span class="sd">    Builder for a param grid used in grid search-based model selection.</span>

<span class="sd">    &gt;&gt;&gt; from pyspark.ml.classification import LogisticRegression</span>
<span class="sd">    &gt;&gt;&gt; lr = LogisticRegression()</span>
<span class="sd">    &gt;&gt;&gt; output = ParamGridBuilder() \</span>
<span class="sd">    ...     .baseOn({lr.labelCol: &#39;l&#39;}) \</span>
<span class="sd">    ...     .baseOn([lr.predictionCol, &#39;p&#39;]) \</span>
<span class="sd">    ...     .addGrid(lr.regParam, [1.0, 2.0]) \</span>
<span class="sd">    ...     .addGrid(lr.maxIter, [1, 5]) \</span>
<span class="sd">    ...     .build()</span>
<span class="sd">    &gt;&gt;&gt; expected = [</span>
<span class="sd">    ...     {lr.regParam: 1.0, lr.maxIter: 1, lr.labelCol: &#39;l&#39;, lr.predictionCol: &#39;p&#39;},</span>
<span class="sd">    ...     {lr.regParam: 2.0, lr.maxIter: 1, lr.labelCol: &#39;l&#39;, lr.predictionCol: &#39;p&#39;},</span>
<span class="sd">    ...     {lr.regParam: 1.0, lr.maxIter: 5, lr.labelCol: &#39;l&#39;, lr.predictionCol: &#39;p&#39;},</span>
<span class="sd">    ...     {lr.regParam: 2.0, lr.maxIter: 5, lr.labelCol: &#39;l&#39;, lr.predictionCol: &#39;p&#39;}]</span>
<span class="sd">    &gt;&gt;&gt; len(output) == len(expected)</span>
<span class="sd">    True</span>
<span class="sd">    &gt;&gt;&gt; all([m in expected for m in output])</span>
<span class="sd">    True</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_param_grid</span> <span class="o">=</span> <span class="p">{}</span>

<div class="viewcode-block" id="ParamGridBuilder.addGrid"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.ParamGridBuilder.addGrid">[docs]</a>    <span class="k">def</span> <span class="nf">addGrid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">param</span><span class="p">,</span> <span class="n">values</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Sets the given parameters in this grid to fixed values.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_param_grid</span><span class="p">[</span><span class="n">param</span><span class="p">]</span> <span class="o">=</span> <span class="n">values</span>

        <span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="ParamGridBuilder.baseOn"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.ParamGridBuilder.baseOn">[docs]</a>    <span class="k">def</span> <span class="nf">baseOn</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Sets the given parameters in this grid to fixed values.</span>
<span class="sd">        Accepts either a parameter dictionary or a list of (parameter, value) pairs.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">dict</span><span class="p">):</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">baseOn</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">())</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">for</span> <span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span> <span class="ow">in</span> <span class="n">args</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">addGrid</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="p">[</span><span class="n">value</span><span class="p">])</span>

        <span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="ParamGridBuilder.build"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.ParamGridBuilder.build">[docs]</a>    <span class="k">def</span> <span class="nf">build</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Builds and returns all combinations of parameters specified</span>
<span class="sd">        by the param grid.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">keys</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_param_grid</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
        <span class="n">grid_values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_param_grid</span><span class="o">.</span><span class="n">values</span><span class="p">()</span>
        <span class="k">return</span> <span class="p">[</span><span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">prod</span><span class="p">))</span> <span class="k">for</span> <span class="n">prod</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">grid_values</span><span class="p">)]</span>

</div></div>
<div class="viewcode-block" id="CrossValidator"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.CrossValidator">[docs]</a><span class="k">class</span> <span class="nc">CrossValidator</span><span class="p">(</span><span class="n">Estimator</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    K-fold cross validation.</span>

<span class="sd">    &gt;&gt;&gt; from pyspark.ml.classification import LogisticRegression</span>
<span class="sd">    &gt;&gt;&gt; from pyspark.ml.evaluation import BinaryClassificationEvaluator</span>
<span class="sd">    &gt;&gt;&gt; from pyspark.mllib.linalg import Vectors</span>
<span class="sd">    &gt;&gt;&gt; dataset = sqlContext.createDataFrame(</span>
<span class="sd">    ...     [(Vectors.dense([0.0]), 0.0),</span>
<span class="sd">    ...      (Vectors.dense([0.4]), 1.0),</span>
<span class="sd">    ...      (Vectors.dense([0.5]), 0.0),</span>
<span class="sd">    ...      (Vectors.dense([0.6]), 1.0),</span>
<span class="sd">    ...      (Vectors.dense([1.0]), 1.0)] * 10,</span>
<span class="sd">    ...     [&quot;features&quot;, &quot;label&quot;])</span>
<span class="sd">    &gt;&gt;&gt; lr = LogisticRegression()</span>
<span class="sd">    &gt;&gt;&gt; grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()</span>
<span class="sd">    &gt;&gt;&gt; evaluator = BinaryClassificationEvaluator()</span>
<span class="sd">    &gt;&gt;&gt; cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)</span>
<span class="sd">    &gt;&gt;&gt; cvModel = cv.fit(dataset)</span>
<span class="sd">    &gt;&gt;&gt; evaluator.evaluate(cvModel.transform(dataset))</span>
<span class="sd">    0.8333...</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="c"># a placeholder to make it appear in the generated doc</span>
    <span class="n">estimator</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span> <span class="s">&quot;estimator&quot;</span><span class="p">,</span> <span class="s">&quot;estimator to be cross-validated&quot;</span><span class="p">)</span>

    <span class="c"># a placeholder to make it appear in the generated doc</span>
    <span class="n">estimatorParamMaps</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span> <span class="s">&quot;estimatorParamMaps&quot;</span><span class="p">,</span> <span class="s">&quot;estimator param maps&quot;</span><span class="p">)</span>

    <span class="c"># a placeholder to make it appear in the generated doc</span>
    <span class="n">evaluator</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
        <span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span> <span class="s">&quot;evaluator&quot;</span><span class="p">,</span>
        <span class="s">&quot;evaluator used to select hyper-parameters that maximize the cross-validated metric&quot;</span><span class="p">)</span>

    <span class="c"># a placeholder to make it appear in the generated doc</span>
    <span class="n">numFolds</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span> <span class="s">&quot;numFolds&quot;</span><span class="p">,</span> <span class="s">&quot;number of folds for cross validation&quot;</span><span class="p">)</span>

    <span class="nd">@keyword_only</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">estimatorParamMaps</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">evaluator</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">numFolds</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3)</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">CrossValidator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
        <span class="c">#: param for estimator to be cross-validated</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">&quot;estimator&quot;</span><span class="p">,</span> <span class="s">&quot;estimator to be cross-validated&quot;</span><span class="p">)</span>
        <span class="c">#: param for estimator param maps</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">estimatorParamMaps</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">&quot;estimatorParamMaps&quot;</span><span class="p">,</span> <span class="s">&quot;estimator param maps&quot;</span><span class="p">)</span>
        <span class="c">#: param for the evaluator used to select hyper-parameters that</span>
        <span class="c">#: maximize the cross-validated metric</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">evaluator</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
            <span class="bp">self</span><span class="p">,</span> <span class="s">&quot;evaluator&quot;</span><span class="p">,</span>
            <span class="s">&quot;evaluator used to select hyper-parameters that maximize the cross-validated metric&quot;</span><span class="p">)</span>
        <span class="c">#: param for number of folds for cross validation</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">numFolds</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">&quot;numFolds&quot;</span><span class="p">,</span> <span class="s">&quot;number of folds for cross validation&quot;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">numFolds</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
        <span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__init__</span><span class="o">.</span><span class="n">_input_kwargs</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_set</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

    <span class="nd">@keyword_only</span>
<div class="viewcode-block" id="CrossValidator.setParams"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.CrossValidator.setParams">[docs]</a>    <span class="k">def</span> <span class="nf">setParams</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">estimator</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">estimatorParamMaps</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">evaluator</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">numFolds</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3):</span>
<span class="sd">        Sets params for cross validator.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">setParams</span><span class="o">.</span><span class="n">_input_kwargs</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="CrossValidator.setEstimator"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.CrossValidator.setEstimator">[docs]</a>    <span class="k">def</span> <span class="nf">setEstimator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Sets the value of :py:attr:`estimator`.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">estimator</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
        <span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="CrossValidator.getEstimator"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.CrossValidator.getEstimator">[docs]</a>    <span class="k">def</span> <span class="nf">getEstimator</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Gets the value of estimator or its default value.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">estimator</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="CrossValidator.setEstimatorParamMaps"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.CrossValidator.setEstimatorParamMaps">[docs]</a>    <span class="k">def</span> <span class="nf">setEstimatorParamMaps</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Sets the value of :py:attr:`estimatorParamMaps`.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">estimatorParamMaps</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
        <span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="CrossValidator.getEstimatorParamMaps"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.CrossValidator.getEstimatorParamMaps">[docs]</a>    <span class="k">def</span> <span class="nf">getEstimatorParamMaps</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Gets the value of estimatorParamMaps or its default value.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">estimatorParamMaps</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="CrossValidator.setEvaluator"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.CrossValidator.setEvaluator">[docs]</a>    <span class="k">def</span> <span class="nf">setEvaluator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Sets the value of :py:attr:`evaluator`.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
        <span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="CrossValidator.getEvaluator"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.CrossValidator.getEvaluator">[docs]</a>    <span class="k">def</span> <span class="nf">getEvaluator</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Gets the value of evaluator or its default value.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="CrossValidator.setNumFolds"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.CrossValidator.setNumFolds">[docs]</a>    <span class="k">def</span> <span class="nf">setNumFolds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Sets the value of :py:attr:`numFolds`.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">numFolds</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
        <span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="CrossValidator.getNumFolds"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.CrossValidator.getNumFolds">[docs]</a>    <span class="k">def</span> <span class="nf">getNumFolds</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Gets the value of numFolds or its default value.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">numFolds</span><span class="p">)</span>
</div>
    <span class="k">def</span> <span class="nf">_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
        <span class="n">est</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">estimator</span><span class="p">)</span>
        <span class="n">epm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">estimatorParamMaps</span><span class="p">)</span>
        <span class="n">numModels</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">epm</span><span class="p">)</span>
        <span class="n">eva</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator</span><span class="p">)</span>
        <span class="n">nFolds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">numFolds</span><span class="p">)</span>
        <span class="n">h</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">nFolds</span>
        <span class="n">randCol</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">uid</span> <span class="o">+</span> <span class="s">&quot;_rand&quot;</span>
        <span class="n">df</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">&quot;*&quot;</span><span class="p">,</span> <span class="n">rand</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">alias</span><span class="p">(</span><span class="n">randCol</span><span class="p">))</span>
        <span class="n">metrics</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">numModels</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nFolds</span><span class="p">):</span>
            <span class="n">validateLB</span> <span class="o">=</span> <span class="n">i</span> <span class="o">*</span> <span class="n">h</span>
            <span class="n">validateUB</span> <span class="o">=</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">h</span>
            <span class="n">condition</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">randCol</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">validateLB</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">randCol</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">validateUB</span><span class="p">)</span>
            <span class="n">validation</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">condition</span><span class="p">)</span>
            <span class="n">train</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="o">~</span><span class="n">condition</span><span class="p">)</span>
            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">numModels</span><span class="p">):</span>
                <span class="n">model</span> <span class="o">=</span> <span class="n">est</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">epm</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
                <span class="c"># TODO: duplicate evaluator to take extra params from input</span>
                <span class="n">metric</span> <span class="o">=</span> <span class="n">eva</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">validation</span><span class="p">,</span> <span class="n">epm</span><span class="p">[</span><span class="n">j</span><span class="p">]))</span>
                <span class="n">metrics</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">+=</span> <span class="n">metric</span>

        <span class="k">if</span> <span class="n">eva</span><span class="o">.</span><span class="n">isLargerBetter</span><span class="p">():</span>
            <span class="n">bestIndex</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">bestIndex</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span>
        <span class="n">bestModel</span> <span class="o">=</span> <span class="n">est</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">epm</span><span class="p">[</span><span class="n">bestIndex</span><span class="p">])</span>
        <span class="k">return</span> <span class="n">CrossValidatorModel</span><span class="p">(</span><span class="n">bestModel</span><span class="p">)</span>

<div class="viewcode-block" id="CrossValidator.copy"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.CrossValidator.copy">[docs]</a>    <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">extra</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">extra</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">extra</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
        <span class="n">newCV</span> <span class="o">=</span> <span class="n">Params</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">extra</span><span class="p">)</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">isSet</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">estimator</span><span class="p">):</span>
            <span class="n">newCV</span><span class="o">.</span><span class="n">setEstimator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">getEstimator</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">extra</span><span class="p">))</span>
        <span class="c"># estimatorParamMaps remain the same</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">isSet</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator</span><span class="p">):</span>
            <span class="n">newCV</span><span class="o">.</span><span class="n">setEvaluator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">getEvaluator</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">extra</span><span class="p">))</span>
        <span class="k">return</span> <span class="n">newCV</span>

</div></div>
<div class="viewcode-block" id="CrossValidatorModel"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.CrossValidatorModel">[docs]</a><span class="k">class</span> <span class="nc">CrossValidatorModel</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Model from k-fold cross validation.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bestModel</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">CrossValidatorModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
        <span class="c">#: best model from cross validation</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">bestModel</span> <span class="o">=</span> <span class="n">bestModel</span>

    <span class="k">def</span> <span class="nf">_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">bestModel</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>

<div class="viewcode-block" id="CrossValidatorModel.copy"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.CrossValidatorModel.copy">[docs]</a>    <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">extra</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Creates a copy of this instance with a randomly generated uid</span>
<span class="sd">        and some extra params. This copies the underlying bestModel,</span>
<span class="sd">        creates a deep copy of the embedded paramMap, and</span>
<span class="sd">        copies the embedded and extra parameters over.</span>
<span class="sd">        :param extra: Extra parameters to copy to the new instance</span>
<span class="sd">        :return: Copy of this instance</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">extra</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">extra</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
        <span class="k">return</span> <span class="n">CrossValidatorModel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bestModel</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">extra</span><span class="p">))</span>

</div></div>
<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">&quot;__main__&quot;</span><span class="p">:</span>
    <span class="kn">import</span> <span class="nn">doctest</span>
    <span class="kn">from</span> <span class="nn">pyspark.context</span> <span class="kn">import</span> <span class="n">SparkContext</span>
    <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SQLContext</span>
    <span class="n">globs</span> <span class="o">=</span> <span class="nb">globals</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
    <span class="c"># The small batch size here ensures that we see multiple batches,</span>
    <span class="c"># even in these small test examples:</span>
    <span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="p">(</span><span class="s">&quot;local[2]&quot;</span><span class="p">,</span> <span class="s">&quot;ml.tuning tests&quot;</span><span class="p">)</span>
    <span class="n">sqlContext</span> <span class="o">=</span> <span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>
    <span class="n">globs</span><span class="p">[</span><span class="s">&#39;sc&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sc</span>
    <span class="n">globs</span><span class="p">[</span><span class="s">&#39;sqlContext&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sqlContext</span>
    <span class="p">(</span><span class="n">failure_count</span><span class="p">,</span> <span class="n">test_count</span><span class="p">)</span> <span class="o">=</span> <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">(</span>
        <span class="n">globs</span><span class="o">=</span><span class="n">globs</span><span class="p">,</span> <span class="n">optionflags</span><span class="o">=</span><span class="n">doctest</span><span class="o">.</span><span class="n">ELLIPSIS</span><span class="p">)</span>
    <span class="n">sc</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
    <span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span>
        <span class="nb">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
</pre></div>

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